is there a case for mobile phone content pre-staging? santa barbara, december 9-12, 2013 alessandro...
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IS THERE A CASE FOR MOBILE PHONE CONTENT
PRE-STAGING?
Santa Barbara, December 9-12, 2013
Alessandro Finamore Marco Mellia Zafar Gilani
Konstantina PapagiannakiYan GrunenbergerVijay Erramilli
Politecnico di Torino
Universitat Politecnica de Catalunya
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Caching in mobile networks
Gn
Proxy-cacheTwo solutions for saving volumes￭Forward caching: add proxy-cache in the network core
Reduce traffic volume only “towards the Internet” Savings are driven by the cache hit-ratio which is typically
~30% (1)
￭Cache on device: reserve specific storage for caching on users’ device
Reduces wireless link traffic on a per-user base The same object requested by different devices traverses
the wireless link at least once for each device
(1) [AT&T – IEEE Int. Comp journal ‘11] To cache or not to cache: The 3g case
local cache
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Caching in mobile networks
Gn
Proxy-cacheTwo solutions available for caching in mobile networks￭Forward caching: add proxy-cache in the network core
Reduce traffic volume only “towards the Internet” Savings are driven by the cache hit-ratio which is typically
~30% (1)
￭Cache on device: reserve specific storage for caching on users’ device
Reduces wireless link traffic on a per-user base The same object requested by different devices traverses
the wireless link at least once for each device
(1) [AT&T – IEEE Int. Comp journal ‘11] To cache or not to cache: The 3g case
local cache
Both are important but none of them really address the most expensive part of the
network:wireless channel
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Key idea: content pre-staging
Gn
Proxy-cache
local cache
Bundle creator
￭The Bundle creator periodically creates a bundle of popular objects￭The bundle is pre-staged to users’ device using a broadcast channel￭Users’ device store and consume the bundle locally
Push-based system: transmit 1copy to serve requests of multiple users
We would like to mute as many requests as possible
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…but is it worth?
￭Rather than designing the system as a whole, we want to quantify the potential gains the system would offer
￭Research questions addressed in this work: Is the content popularity skewed enough to allow
gain? How large should the bundle be to lead to
savings? What are the achievable saving? Any benefits for the users?
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Real data: geo-tagged HTTP logs
Real trace collected from a major European mobile carrier￭1 day from a large metropolis￭HTTP logs created by forward caches
Anonymised terminal ID, URL, downloaded volume
Cached-flag, indicating hit/miss by forward caches
Cell-ID, indicating from which cell requests are issued
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Good news: content is pre-stageable!
￭Popularity: the top 1,000 requested objects
account for 15% of byte-wise volume and for 48% of req.
are accessed at least once by 80% of customers
￭Cacheability: ~40% of objects are cached by proxies for the evaluation we consider a conservative and
aoptimistic scenarios
￭Long lifetime: 95% of cacheable objects have a lifetime >1hour
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Which bundle creator strategies?
￭What to bundle? Max-Req: bundle top-N most popular objects Max-Vol: bundle top-N objects generating the
largest volume Weighted-Vol: bundle big and popular objects
￭How often to broadcast? Engineering choice: every hour is a reasonable
value1. Create the bundle based on the traffic
between 5pm-6pm2. Broadcast the bundle at 6pm3. Users’ terminals enjoy the content between
6pm-7pm
￭Same bundle broadcasted to all cells
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3,000 objects are enough to get saving
￭With simply 3,000 objects we can achieve savings￭Max-Vol has the highest volume savings (>13%, conservative)
... but how big is the bundle?
0.13
For Max-Vol3,000 objects = >500MB!!!
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￭With simply 3,000 objects we can achieve savings￭Max-Vol has the highest volume savings (>13%, conservative)
... but how big is the bundle? 3,000 objects = more than 500MB
It is not a practical solution￭Max-Req performs as Weighted-Vol but has a simpler logic
0.13
For Max-Vol3,000 objects = >500MB!!!
0.07
3,000 objects are enough to get saving
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￭Overall, Max-Req is the best strategy: With 3,000 objects = only 34MB
7% volume saved (conservative) 11% requests saved (conservative)
0.11
34MB = at least 7% of savings
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Saving are stable over time
Results stableacross the day!!!
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15% of users become totally silent
￭20% of users does not benefit Heavy hitters downloading only
few big (and unpopular) objects￭15% of users enjoys 100% of savings
Users accessing GPS and navigation services
￭Similar results for num. of requests (details in the paper)
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Spatial correlation
￭What if we consider a per-tower bundle? Consider 2 towers (periphery and downtown) of the
top10 towers generating the largest number of requests and volume
Focus on peak hour
per-tower bundleis sub-optimal
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Conclusions & Future work
￭Results show that pre-staging could be an opportunity to optimize wireless capacity
BUT￭Reception costs and other aspects need further investigation
Device energy consumption System engineering
Spatial and temporal correlations Both content providers and users can collaborate
with the system to estimate content popularity Returns depend also on costs and the wiliness of
operators to invest in such technology
